A large cohort implies a large heterogeneity of the patient population, geographical distribution

The gap

Evidence profile

Sourced from the limitations section and limitations and recommendations of the source papers, classified as general, spanning 4 journals. Those papers have been cited 2 times in total.

Supporting evidence — 4 representative gaps

• Impact of appropriate antimicrobial therapy on patient outcomes and antimicrobial use: a sub analysis of the DIANA Study Dataset (2026) · Intensive Care Medicine · cited 2× · doi

  a large cohort implies a large heterogeneity of the patient population, geographical distribution, clinical characteristics and considerably large rates of local MDR profiles and antimicrobial stewardship - our definition of appropriateness was microbiologically anchored, it may have been influenced by local antimicrobial resistance patterns and the relatively high rate of combination and broad-spectrum antimicrobial therapies overestimating appropriateness by increasing microbiological coverage rather than precision or timing - the relatively low rate of inappropriate empirical therapy (12.3%) limited statistical power for some subgroup analyses, especially at higher illness severities, and at certain higher SOFA scores in specific organ dysfunction domains - SOFA scores were used as markers of illness severity, these do not fully reflect dynamic clinical evolution or delayed organ dysfunction

  generallimitations section
  Keywords: large cohort implies heterogeneity patient population geographical distribution
• Pharmacotherapeutic Assessment of Three Major Infectious Disorders in District Abbottabad and Haripur (2026) · The Open Public Health Journal · doi

  Despite the valuable insights provided by the current pharmacotherapeutic assessment of three major infectious disorders, certain limitations of the study included a retrospective analysis, including the data extracted from hospital records of case profiles that might be associated with poor documentation quality, potential recall errors, and incomplete diagnostic or therapeutic profiles. Though 300 profiles have been observed, some intervening factors, such as socioeconomic and nutritional factors, and previous utilization or adherence to antimicrobials, have not been accurately covered. Secondly, the data was including JIH collected from two hospitals only, (Abbottabad) and DHQ hospital (Haripur), which may not represent the entire regional population, leading to potential bias due to differences in demographic and clinical outcomes of the patients belonging to regional or rural health care centers. The selection of broader and more diverse health care settings might enhance generalizability. Similarly, the micro-organism’s susceptibility and resistance pattern towards the selected antimicrobial medicaments were not evaluated in the current study, which might be correlated to check the pathogen-specific therapeutic efficacy of antimicrobial drugs. The study could be more from inclusion of microbial cultures and antibiograms to effectively check the therapeutic efficacy of prescribed antibiotics, particularly against the resistant bacterial strains (such as S. Typhi, E. coli, and S. Pneumoniae). Though sex disaggregated prevalence was included, particularly sex specific therapeutic variabilities were not assessed in detail. Similarly, gender associated socio- cultural determinants were not measured quantitatively, limiting their impact on risk of disease occurrence, progression, and therapeutic outcome. Furthermore, the study only covered Drug-Drug interactions, while not quantifying the pharmacokinetic variations among cases of variable age groups or patients with comorbidities leading to variable therapeutic responses, particularly among the geriatric and mal- nourished population.

  generallimitationsevidence 5/5
  Keywords: therapeutic profiles particularly current included including hospital associated potential though factors covered regional population leading
• Blood Culture Utilization, Contamination Rates, Antibiotic Use, and Patient Outcomes in Intensive Care Units at a Tertiary Care Hospital in Western India: A Prospective Observational Study (2026) · Cureus · doi

  A major strength of this study lies in its 12-month prospective observational design and the integration of microbiological profiles, antimicrobial utilization metrics, and patient outcomes within a unified epidemiological framework. Furthermore, the inclusion of diverse intensive care populations, spanning adult (MICU), neonatal (NICU), and pediatric (PICU) settings, provides a highly comprehensive, real-world picture of baseline BC practices and the localized MDR burden. Our study has several important limitations that must be acknowledged. First, in addition to the single- center observational design and lack of longitudinal time-series tracking, no formal diagnostic stewardship intervention was implemented during the study period; therefore, the data represent baseline epidemiology rather than interventional outcomes. Second, due to data collection constraints, we were unable to incorporate standardized severity-of-illness scores (such as APACHE II or SOFA) or assess the precise timing and appropriateness of the initial empirical antibiotic therapies administered. Finally, our primary outcome and antibiotic consumption analyses were explicitly restricted to the subset of culture-positive patients (n = 142). While this allowed for a focused evaluation of definitive targeted therapy, it inherently limits the ability to draw broad inferences regarding the overall impact of pre-analytical stewardship on the wider culture-negative ICU population.

  generallimitationsevidence 5/5
  Keywords: observational design outcomes baseline stewardship antibiotic culture major strength lies month prospective integration microbiological profiles
• Current perspectives on surgical antibiotic prophylaxis: guideline recommendations, prescribing patterns, antimicrobial resistance and future directions (2026) · International Journal of Research in Medical Sciences · doi

  infections. Ann Surg. 2011;253(6):1082-93. 43. Hou Y, Collinsworth A, Hasa F, Griffin L. Incidence and impact of surgical site infections on length of stay and cost of care for patients undergoing open procedures. Surg Open Sci. 2022;11:1-18. 44. Barth JH, Misra S, Aakre KM, Langlois MR, Watine J, Twomey PJ, et al. Why are clinical practice guidelines not followed? Clin Chem Lab Med. 2016;54(7):1133-9. 45. Schuts EC, Hulscher MEJL, Mouton JW, Verduin CM, Stuart JWTC, Overdiek HWPM, et al. Current evidence on hospital antimicrobial stewardship objectives: a systematic review and meta-analysis. Lancet Infect Dis. 2016;16(7):847-56. 46. Broom A, Broom J, Kirby E, Gibson A, Davis M. Antibiotic optimisation in 'the bush': local know-how and relations. Health Place. 2017;48:56-62. core-periphery 47. Ayukekbong JA, Ntemgwa M, Atabe AN. The threat of antimicrobial resistance in developing countries: causes and control strategies. Antimicrob Resist Infect Control. 2017;6:47. 48. Broom J, Broom A, Adams K, Plage S. What prevents the intravenous to oral antibiotic switch? A qualitative study of hospital doctors' accounts of what influences their clinical practice. J Antimicrob Chemother. 2016;71(8):2295-9. 49. Curtis CE, Bahar FA, Marriott JF. The effectiveness of computerised decision support on antibiotic use in hospitals: review. PLoS One. 2017;12(8):e0183062. systematic a 50. Pollack LA, van Santen KL, Weiner LM, Dudeck JR, Srinivasan A. Antibiotic MA, Edwards stewardship programs in U.S. acute care hospitals: findings from the 2014 National Healthcare Safety Network annual hospital survey. Clin Infect Dis. 2016;63(4):443-9. 51. World Health Organization. Antimicrobial resistance. at: Geneva: WHO; https://www.who.int/news-room/fact- sheets/detail/antimicrobial-resistance. Accessed on 1 June 2026.

  generalrecommendationsevidence 5/5
  Keywords: antimicrobial broom antibiotic hospital infect resistance infections surg care open clinical practice clin stewardship systematic

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